This document discusses project management and big data analytics. It covers two main topics: 1) Project management of analytics and analytics of project management. It discusses the roles of data scientists and project managers. It also identifies common myths and facts regarding big data/analytics projects and project management. Key reasons for big data project failures are discussed, along with success factors such as getting a data scientist involved, developing a clear business case, using appropriate tools and methodologies, and adopting agile principles.

1. PROJECT MANAGEMENT &
BIG DATA ANALYTICS
Sandeep Kumar PMP®

2. INTRODUCTION
IT Strategy & Business Transformation
Industries:
Media & Advertising, Telecom, BFSI,
FMCG, Manufacturing, BPO/KPO
Services:
Shared Service Delivery, GIC & Back
Office, PMO, Lean Six Sigma, Continuous
Improvement, Enterprise IT, ERP, Cloud &
Infrastructure, Development, Outsourcing
and IT Security & Governance

3. TWO STREAMS
Project Management of
Analytics
• Big Data
• Data Warehousing
• Lean Six Sigma
• In-memory computing
• Internet of things
• Social Media
Analytics of Project
Management
• Time-Cost-Spec Analytics
• Feasibility Analytics
• Resource Analytics
• Management of Collaboration
• Agile & SCRUM

4. APOLOGIES, DISCLAIMERS, ET AL
• Big Data is over-hyped
• Big Data is still evolving
• Analytics is old, the tools are new!
• Project Management solves most of the problems
• Its importance is usually understated
• The success of Big Data initiative lies primarily on the management, then
on the PM & the DS
• Hold the PM responsible only if you know what you want!
• The roles I talk about here are essentially with respect Big Data Projects
If you have
Questions,
I will try &
Answer
them!

5. UNDER SCANNER !
Big Data / Analytics
Myths
• It is mature and cool
• Is an extension of EDW
• Data Quality can be slightly compromised
• A single pre-built technology (e.g. Hadoop) will suffice
• Data scientists are easy to get
• Virtualization / Clustering will take care of infra needs
• If you have huge data, every solution is a Big Data solution
Project Management
Myths
• Managing only activities
• Just time and cost management
• Mere resource allocation
• Reaching the finishing-line!
• General management suffices
• Have time to learn

6. UNDER SCANNER !
Big Data / Analytics
Facts
• Responsible for Business Case and ROI
definitions
• Executive Sponsorship & Funds
• ‘Real’ Resource Provisioning
• Based on Enterprise Architecture
• Highly complex and iterative process
• Loads of scientific knowledge required
• Source of data increases every day
• Should be able to adapt with time
Project Management
Facts
• Responsible for Scope & acceptance by all
parties
• Direction setting & KPI-SF definitions
• ‘Real’ Resource Management
• Right to procure & deploy the appropriate
resource
• Stakeholder & Communication management
• Accountability and Responsibility for the success
(and failure)

7. THE DATA SCIENTIST
Key skills of a Data Scientist – the hard skills guy
• Basic Tools: Knowledge of statistical programming languages, like R or Python, and SQL
• Basic Statistics: Familiar with statistical tests, distributions, maximum likelihood estimators, etc.
• ETL Tools: Best in class like Informatica, IBM Infosphere, SAP BO, Oracle or SAS Data Integrator, Penta-ho, AB-Initio
• Machine Learning / Artificial Intelligence / Pattern Recognition: Methods for Classification and Regression like k-nearest
neighbours, random forests, etc.
• Multivariable Calculus & Linear Algebra: Specially required where data is used for predictive performance or algorithm
optimization
• Data Munging / Scrubbing or Cleanliness: For example inconsistent string formatting as ND or Del for New Delhi; date
alignment as [mm-dd-yyyy] or [dd-mm-yyyy] or [yyyy-dd-mm]
• Data Visualization & Communication tools: Principles of and tools of Data Visualization like ggplot and d3.js.
• Software Engineering: Strong software engineering background, SDLC, Agile, Scrum, DB techniques, Data intensive product development
• Software Testing skills – To make sure the output delivers what the business needs
• Basic Project Management Skills: Thinking like a Project Manager

8. THE PROJECT MANAGER
Key skills of a Project Manager – the soft skills guy
• Project Charter: Project Stakeholders and Objectives documented and signed-off
• Business Case: Asks the ‘whats’ and ‘whys’ of the business requirement
• Scheduling Tools: Creates a Plan of Action to answer the ‘hows’ of the project
• Vendor Management: Links up all 1st and 3rd party resources
• Risk Management: The real management tool, with the mitigant
• Communication Management: The core of collaboration and Management
• Software Engineering: Software engineering background with fair knowledge of tools
• Software Testing skills: To make sure the output delivers what the business needs
• Basic Data Management Skills: Thinking like a Data Scientist

9. MERGING ROLES
The Data Scientist
 The Requirements guy
 The Data Tools guy
 The Resource guy
 The Specialist
 The Enterprise Architect
 The Software guy
Breaking the Technical Barriers
The Project Manager
 The Requirements & Scope guy
 The Project Tools guy
 The Resource guy
 The Generalist
 The Program Manager
 The hardware & Software guy
Breaking the Cultural Barriers

10. WHY DO ANALYTICS
PROJECTS FAIL ?
When do Projects fail, in general?
• Not completed within budgets
• Not completed on time
• Not completed as per specifications
Whys and Wherefores…
• Poor scoping – unclear objective
• Inadequate resources – lack of talent
• Inappropriate Solution – wrong tool selection
• Bad planning – Insufficient analysis
• Bad execution – poor Project Management
“3 out of 4 Big Data Projects fail”
• Inaccurate Project Scope
• Lack of Talent
• Challenging Tools
• Even more Challenging Concepts
• Poor Planning
• Ownership Issues – Business
Initiative or IT Project?
Apache Hadoop vs. Apache Cassandra
55% of Big Data projects don’t get completed; in case of IT projects in general, it is only 25%.

11. THE SUCCESS MANTRA!
• Get a Data Scientist at the PM or SME
• Or, at least get the Data Scientist as one of the leads
• Ask very tough questions to sponsors for a Business Case
• Get the CFO and the End-User on your side – get the expectations right
• Take your time – use appropriate Project Management methodologies
• Use the most appropriate Platform / Tool
• Accept requirements’ volatility
• Scrum: accepting that the problem cannot be fully understood or defined, focusing instead on maximizing the team's ability to deliver quickly
• POC  Deployment  Acceptance  Enhancement  Deployment … …
• Agile: adaptive planning, evolutionary development, early delivery, continuous improvement
• Document and Handover to End-User at every stage
PM answers “How” as long as the Business knows “Why & What”

12. QUESTIONS, ANY?